{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a6fb3675-b791-4647-8db2-43018c4ed933",
   "metadata": {},
   "source": [
    "# Sliceguard for AutoML\n",
    "Sliceguard can be applied for doing a quick modeling of your problem in few lines of code.\n",
    "\n",
    "It offers the following advantages over other libraries:\n",
    "1. Handles feature encoding and normalization automatically\n",
    "2. Supports mixing structured and unstructured data out of the box\n",
    "3. Hyperparameter Tuning is also built-in using FLAML\n",
    "4. Built-in interactive report including problematic slices and prediction explanations.\n",
    "\n",
    "Note that sliceguard does not aim at replacing any AutoML tooling out there. Its use lies mostly in the quick modeling of a problem in a very opinionated way to get a first glance on the problems you might be dealing with and to evaluate the feasability of use cases and datasets in general."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cafb6c15-725a-46bd-9a6d-d0fafdb93365",
   "metadata": {},
   "source": [
    "# Example\n",
    "\n",
    "In the following example we want to estimate the subjective rating of a wine using a mix of structured and unstructured data. Sliceguard thus has to make use of structured features as well a unstructured features like text data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1727ad1f-7064-4d79-bf30-90eb316e31c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Sliceguard\n",
    "from sliceguard import SliceGuard\n",
    "from sliceguard.data import from_huggingface\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score, mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0eec7990-d512-4fde-9673-ce6abb17a59b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the data\n",
    "df = from_huggingface(\"alfredodeza/wine-ratings\")\n",
    "df = df.sample(2000) # subsample for quicker execution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "75a41d03-b947-4a1d-8faa-56292bf6623f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>region</th>\n",
       "      <th>variety</th>\n",
       "      <th>rating</th>\n",
       "      <th>notes</th>\n",
       "      <th>split</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30215</th>\n",
       "      <td>Kim Crawford Sauvignon Blanc 2012</td>\n",
       "      <td>Marlborough, New Zealand</td>\n",
       "      <td>White Wine</td>\n",
       "      <td>91.0</td>\n",
       "      <td>Upfront herbaceous aromas, backed by ripe frui...</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3468</th>\n",
       "      <td>Banfi Le Rime Pinot Grigio 2011</td>\n",
       "      <td>Tuscany, Italy</td>\n",
       "      <td>White Wine</td>\n",
       "      <td>87.0</td>\n",
       "      <td>Pale straw yellow. Hints of pear and white flo...</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6285</th>\n",
       "      <td>Booker Vineyard My Favorite Neighbor 2010</td>\n",
       "      <td>Paso Robles, Central Coast, California</td>\n",
       "      <td>Red Wine</td>\n",
       "      <td>92.0</td>\n",
       "      <td>This version of MFN has many more layers and m...</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14830</th>\n",
       "      <td>Clos Saint-Jean Chateauneuf-du-Pape Blanc 2011</td>\n",
       "      <td>Chateauneuf-du-Pape, Rhone, France</td>\n",
       "      <td>White Wine</td>\n",
       "      <td>92.0</td>\n",
       "      <td>A blend of equal parts Grenache, Clairette and...</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10568</th>\n",
       "      <td>Chateau de Beaucastel Chateauneuf-du-Pape Blan...</td>\n",
       "      <td>Chateauneuf-du-Pape, Rhone, France</td>\n",
       "      <td>White Wine</td>\n",
       "      <td>92.0</td>\n",
       "      <td>The white wines of Château de Beaucastel are a...</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18799</th>\n",
       "      <td>Domaine Huet Vouvray Haut Lieu Demi-Sec (375ML...</td>\n",
       "      <td>Vouvray, Touraine, Loire, France</td>\n",
       "      <td>White Wine</td>\n",
       "      <td>96.0</td>\n",
       "      <td>Taking its roots deep into the chalk soils of ...</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21994</th>\n",
       "      <td>Famille Perrin Reserve Cotes du Rhone Blanc 2007</td>\n",
       "      <td>Cotes du Rhone, Rhone, France</td>\n",
       "      <td>White Wine</td>\n",
       "      <td>88.0</td>\n",
       "      <td>Jean Pierre and Francois Perrin have taken par...</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22572</th>\n",
       "      <td>Fess Parker Santa Barbara Chardonnay 2000</td>\n",
       "      <td>Central Coast, California</td>\n",
       "      <td>White Wine</td>\n",
       "      <td>89.0</td>\n",
       "      <td>You will find notes of tropical fruits, citrus...</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15814</th>\n",
       "      <td>Cottanera Etna Bianco 2017</td>\n",
       "      <td>Sicily, Italy</td>\n",
       "      <td>White Wine</td>\n",
       "      <td>91.0</td>\n",
       "      <td>100% Carricante from vineyards that sit at 720...</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23134</th>\n",
       "      <td>Foley Estate Winery Two Sisters Chardonnay 2008</td>\n",
       "      <td>Central Coast, California</td>\n",
       "      <td>White Wine</td>\n",
       "      <td>95.0</td>\n",
       "      <td>Our 2008 Two Sisters Chardonnay is a stunning ...</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    name  \\\n",
       "30215                  Kim Crawford Sauvignon Blanc 2012   \n",
       "3468                     Banfi Le Rime Pinot Grigio 2011   \n",
       "6285           Booker Vineyard My Favorite Neighbor 2010   \n",
       "14830     Clos Saint-Jean Chateauneuf-du-Pape Blanc 2011   \n",
       "10568  Chateau de Beaucastel Chateauneuf-du-Pape Blan...   \n",
       "...                                                  ...   \n",
       "18799  Domaine Huet Vouvray Haut Lieu Demi-Sec (375ML...   \n",
       "21994   Famille Perrin Reserve Cotes du Rhone Blanc 2007   \n",
       "22572          Fess Parker Santa Barbara Chardonnay 2000   \n",
       "15814                         Cottanera Etna Bianco 2017   \n",
       "23134    Foley Estate Winery Two Sisters Chardonnay 2008   \n",
       "\n",
       "                                       region     variety  rating  \\\n",
       "30215                Marlborough, New Zealand  White Wine    91.0   \n",
       "3468                           Tuscany, Italy  White Wine    87.0   \n",
       "6285   Paso Robles, Central Coast, California    Red Wine    92.0   \n",
       "14830      Chateauneuf-du-Pape, Rhone, France  White Wine    92.0   \n",
       "10568      Chateauneuf-du-Pape, Rhone, France  White Wine    92.0   \n",
       "...                                       ...         ...     ...   \n",
       "18799        Vouvray, Touraine, Loire, France  White Wine    96.0   \n",
       "21994           Cotes du Rhone, Rhone, France  White Wine    88.0   \n",
       "22572               Central Coast, California  White Wine    89.0   \n",
       "15814                           Sicily, Italy  White Wine    91.0   \n",
       "23134               Central Coast, California  White Wine    95.0   \n",
       "\n",
       "                                                   notes  split  \n",
       "30215  Upfront herbaceous aromas, backed by ripe frui...  train  \n",
       "3468   Pale straw yellow. Hints of pear and white flo...  train  \n",
       "6285   This version of MFN has many more layers and m...  train  \n",
       "14830  A blend of equal parts Grenache, Clairette and...  train  \n",
       "10568  The white wines of Château de Beaucastel are a...  train  \n",
       "...                                                  ...    ...  \n",
       "18799  Taking its roots deep into the chalk soils of ...  train  \n",
       "21994  Jean Pierre and Francois Perrin have taken par...  train  \n",
       "22572  You will find notes of tropical fruits, citrus...  train  \n",
       "15814  100% Carricante from vineyards that sit at 720...  train  \n",
       "23134  Our 2008 Two Sisters Chardonnay is a stunning ...  train  \n",
       "\n",
       "[2000 rows x 6 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the data\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dfffb850-3a6d-4840-9631-af2b6bf8601d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data\n",
    "train_df, test_df = train_test_split(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "77ed3aea-d4d0-4a60-bf45-893f1ee9e53b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature name was inferred as referring to raw data. If this is not the case, please specify in feature_types!\n",
      "Feature region was inferred as being categorical. Will be treated as nominal by default. If ordinal specify in feature_types and feature_orders or use Pandas categoricals!\n",
      "Feature variety was inferred as being categorical. Will be treated as nominal by default. If ordinal specify in feature_types and feature_orders or use Pandas categoricals!\n",
      "Feature notes was inferred as referring to raw data. If this is not the case, please specify in feature_types!\n",
      "Using default model for computing embeddings for feature name.\n",
      "Warning: Column name will be treated as text. If the column name is a path to some file it is probably not supported yet!\n",
      "Embedding computation on cuda with batch size 1 and multiprocessing None.\n",
      "Pre-reducing feature name in mode automl.\n",
      "Using op mix ratio 0.8.\n",
      "Using num dimensions 8.\n",
      "Using default model for computing embeddings for feature notes.\n",
      "Warning: Column notes will be treated as text. If the column notes is a path to some file it is probably not supported yet!\n",
      "Embedding computation on cuda with batch size 1 and multiprocessing None.\n",
      "Pre-reducing feature notes in mode automl.\n",
      "Using op mix ratio 0.8.\n",
      "Using num dimensions 8.\n",
      "[flaml.automl.logger: 12-04 10:29:25] {1679} INFO - task = regression\n",
      "[flaml.automl.logger: 12-04 10:29:25] {1690} INFO - Evaluation method: holdout\n",
      "[flaml.automl.logger: 12-04 10:29:25] {1788} INFO - Minimizing error metric: mse\n",
      "[flaml.automl.logger: 12-04 10:29:25] {1900} INFO - List of ML learners in AutoML Run: ['xgboost']\n",
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      "[flaml.automl.logger: 12-04 10:30:20] {2218} INFO - iteration 125, current learner xgboost\n",
      "[flaml.automl.logger: 12-04 10:30:24] {2391} INFO -  at 58.9s,\testimator xgboost's best error=3.1550,\tbest estimator xgboost's best error=3.1550\n",
      "[flaml.automl.logger: 12-04 10:30:24] {2218} INFO - iteration 126, current learner xgboost\n",
      "[flaml.automl.logger: 12-04 10:30:25] {2391} INFO -  at 60.0s,\testimator xgboost's best error=3.1550,\tbest estimator xgboost's best error=3.1550\n",
      "[flaml.automl.logger: 12-04 10:30:25] {2627} INFO - retrain xgboost for 0.1s\n",
      "[flaml.automl.logger: 12-04 10:30:25] {2630} INFO - retrained model: XGBRegressor(base_score=None, booster=None, callbacks=[],\n",
      "             colsample_bylevel=0.8572105654348214, colsample_bynode=None,\n",
      "             colsample_bytree=0.553402920439985, early_stopping_rounds=None,\n",
      "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
      "             gamma=None, gpu_id=None, grow_policy='lossguide',\n",
      "             importance_type=None, interaction_constraints=None,\n",
      "             learning_rate=0.3274288272027713, max_bin=None,\n",
      "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
      "             max_delta_step=None, max_depth=0, max_leaves=4,\n",
      "             min_child_weight=1.8128000356620766, missing=nan,\n",
      "             monotone_constraints=None, n_estimators=48, n_jobs=-1,\n",
      "             num_parallel_tree=None, predictor=None, random_state=None, ...)\n",
      "[flaml.automl.logger: 12-04 10:30:25] {1930} INFO - fit succeeded\n",
      "[flaml.automl.logger: 12-04 10:30:25] {1931} INFO - Time taken to find the best model: 38.514963150024414\n",
      "The overall metric value is 1.3538483632405598\n",
      "You didn't specify metric_mode. For task regression using min as a default.\n",
      "Detecting issues for criteria n_slices=20, criterion=drop, min_drop=None, min_support=None.\n",
      "Identified 20 problematic slices.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
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       "    'mode': 'Pomerol, Bordeaux, France'},\n",
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       "    'mode': 'Cote Rotie, Rhone, France'}]},\n",
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       "    'mode': 'Napa Valley, California'}]},\n",
       " {'id': 17,\n",
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       "   {'column': 'notes', 'importance': 0.4945561330883823},\n",
       "   {'column': 'variety', 'importance': 0.0, 'mode': 'Red Wine'},\n",
       "   {'column': 'region',\n",
       "    'importance': 0.0,\n",
       "    'mode': 'St. Estephe, Bordeaux, France'}]},\n",
       " {'id': 18,\n",
       "  'level': 3,\n",
       "  'indices': array([12713, 12703]),\n",
       "  'rows': array([815, 882]),\n",
       "  'metric': 3.0,\n",
       "  'explanation': [{'column': 'name', 'importance': 0.9835622164574823},\n",
       "   {'column': 'region',\n",
       "    'importance': 0.016437783542517594,\n",
       "    'mode': 'St. Estephe, Bordeaux, France'},\n",
       "   {'column': 'notes', 'importance': 0.0},\n",
       "   {'column': 'variety', 'importance': 0.0, 'mode': 'Red Wine'}]},\n",
       " {'id': 19,\n",
       "  'level': 3,\n",
       "  'indices': array([ 6827,  9088, 23068]),\n",
       "  'rows': array([1099, 1137, 1301]),\n",
       "  'metric': 3.4093729654947915,\n",
       "  'explanation': [{'column': 'name', 'importance': 0.5940055559141353},\n",
       "   {'column': 'notes', 'importance': 0.40599444408586455},\n",
       "   {'column': 'variety', 'importance': 0.0, 'mode': 'Red Wine'},\n",
       "   {'column': 'region',\n",
       "    'importance': 0.0,\n",
       "    'mode': 'Sonoma County, California'}]}]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the model\n",
    "sg = SliceGuard()\n",
    "sg.fit(train_df, \"rating\", features=[\"name\", \"region\", \"variety\", \"notes\"], task=\"regression\", time_budget=60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ee089f16-5d02-485f-94ee-b82dd33b0390",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Embedding computation on cuda with batch size 1 and multiprocessing None.\n",
      "Embedding computation on cuda with batch size 1 and multiprocessing None.\n"
     ]
    }
   ],
   "source": [
    "# Generate predictions\n",
    "preds = sg.predict(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "539a64ad-59df-49b8-b502-06cd38ce974b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2-score of the model is 0.13445270169148438\n",
      "The MAE of the model is 1.7744297981262207\n"
     ]
    }
   ],
   "source": [
    "# Print model metrics\n",
    "print(f\"The r2-score of the model is {r2_score(test_df['rating'], preds)}\")\n",
    "print(f\"The MAE of the model is {mean_absolute_error(test_df['rating'], preds)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6c15b1d9-6252-417c-8927-e250e0fe8f50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Embedding computation on cuda with batch size 1 and multiprocessing None.\n",
      "Embedding computation on cuda with batch size 1 and multiprocessing None.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x950 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate explanations\n",
    "shap_explanations = sg.explain(test_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5838506e-9837-47ca-9267-a2ef1130ebdf",
   "metadata": {},
   "source": [
    "You already get some insights from the beeswarm plot that is done using the shape library. E.g. that red wines are generally higher rated in the dataset and that also some regions are corresponding to higher or lower ratings. There is also a lot of information in the unstructured text data that the model uses. However, you would have to look into the data to find out why this is the case. However, note that you can definitely do that as the method gives you a \"SHAP Explanation Object\" that you can use for further investigation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d771f34b-5d5b-47d9-b9f0-2bde7a3d4a12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(                                                    name  \\\n",
       " 4250   Berger Lossterrassen Kremstal DAC Gruner Veltl...   \n",
       " 24059                             Gagliole Valletta 2014   \n",
       " 4972               Bleasdale Langhorne Creek Shiraz 2000   \n",
       " 6813          Bravium Rosella's Vineyard Chardonnay 2014   \n",
       " 15101                           Colosi Nero d'Avola 2008   \n",
       " ...                                                  ...   \n",
       " 26914               Hermanos Pecina Senorio Crianza 2000   \n",
       " 29655                  Kay Brothers Hillside Shiraz 2010   \n",
       " 5557                           Bodegas Muga Reserva 2009   \n",
       " 17897  Domaine de la Cote Sta. Rita Hills Pinot Noir ...   \n",
       " 23394                       Four Graces Pinot Blanc 2017   \n",
       " \n",
       "                                                   region     variety  rating  \\\n",
       " 4250                                   Kremstal, Austria  White Wine    93.0   \n",
       " 24059                                     Tuscany, Italy    Red Wine    91.0   \n",
       " 4972                          South Australia, Australia    Red Wine    87.0   \n",
       " 6813   Santa Lucia Highlands, Monterey, Central Coast...  White Wine    90.0   \n",
       " 15101                                      Sicily, Italy    Red Wine    89.0   \n",
       " ...                                                  ...         ...     ...   \n",
       " 26914                                       Rioja, Spain    Red Wine    90.0   \n",
       " 29655           McLaren Vale, South Australia, Australia    Red Wine    90.0   \n",
       " 5557                                        Rioja, Spain    Red Wine    91.0   \n",
       " 17897  Sta. Rita Hills, Santa Barbara, Central Coast,...    Red Wine    93.0   \n",
       " 23394                          Willamette Valley, Oregon  White Wine    90.0   \n",
       " \n",
       "                                                    notes  split  \\\n",
       " 4250   Some \"exotic\" aromas over the classic loess pr...  train   \n",
       " 24059  This red is made of Merlot and Sangiovese. The...  train   \n",
       " 4972   Upfront red fruits,plum and raspberry, a touch...  train   \n",
       " 6813   Rosella's Vineyard is one of the most renowned...  train   \n",
       " 15101  Color is intense ruby red.  Intense blackberry...  train   \n",
       " ...                                                  ...    ...   \n",
       " 26914  \"Light red. Explosive, perfumed nose offers a ...  train   \n",
       " 29655  Vibrant and dark purple in color, the 2010 Hil...  train   \n",
       " 5557   Purplish-red with an even robe and barely tran...  train   \n",
       " 17897  This wine was fermented with 50% whole bunches...  train   \n",
       " 23394  The 2017 Pinot Blanc is bright and refreshing....  train   \n",
       " \n",
       "                                     sg_projection  \\\n",
       " 4250    [0.02182537076878313, 0.2654234460875082]   \n",
       " 24059  [-0.7706465651874488, 0.32008753711833876]   \n",
       " 4972    [-0.602182505421125, 0.23834918257270338]   \n",
       " 6813   [0.34311203433803067, -0.4988060838167729]   \n",
       " 15101   [-0.7607379436355102, 0.2693545237255783]   \n",
       " ...                                           ...   \n",
       " 26914   [-0.8306099485685526, 0.2828985115806502]   \n",
       " 29655   [-0.5948484007234115, 0.2404928965985151]   \n",
       " 5557   [-0.8300895400665744, 0.28217627160043374]   \n",
       " 17897  [-0.5619059452116023, 0.24181558744539772]   \n",
       " 23394   [0.268222110758629, -0.13853943070579872]   \n",
       " \n",
       "                                              sg_emb_name  \\\n",
       " 4250   [-0.14757242798805237, 0.0434730239212513, 0.0...   \n",
       " 24059  [0.017486801370978355, 0.05328171327710152, 0....   \n",
       " 4972   [-0.03195321187376976, 0.007483001798391342, -...   \n",
       " 6813   [0.008941461332142353, 0.019693812355399132, -...   \n",
       " 15101  [-0.06359544396400452, -0.0015200591878965497,...   \n",
       " ...                                                  ...   \n",
       " 26914  [-0.0736122876405716, 0.09094591438770294, -0....   \n",
       " 29655  [-0.03475227579474449, -0.010573362931609154, ...   \n",
       " 5557   [-0.08742330223321915, 0.05779438093304634, -0...   \n",
       " 17897  [0.0018595902947708964, -0.008997932076454163,...   \n",
       " 23394  [-0.033691175282001495, 0.03387785330414772, -...   \n",
       " \n",
       "                                             sg_emb_notes  sg_y_pred  \n",
       " 4250   [-0.05797984451055527, -0.01847716234624386, 0...  91.002060  \n",
       " 24059  [-0.028928807005286217, -0.016591865569353104,...  91.437614  \n",
       " 4972   [0.006347679533064365, -0.06107068806886673, 0...  90.167999  \n",
       " 6813   [0.069998599588871, -0.00487137446179986, -0.0...  90.814941  \n",
       " 15101  [-0.03763904422521591, -0.05443495884537697, -...  90.597694  \n",
       " ...                                                  ...        ...  \n",
       " 26914  [0.020768743008375168, -0.10751371830701828, -...  90.682693  \n",
       " 29655  [0.037709012627601624, -0.05015264078974724, -...  91.022415  \n",
       " 5557   [-0.00806320272386074, 0.010528632439672947, -...  91.112221  \n",
       " 17897  [0.009952976368367672, -0.0027751591987907887,...  91.510971  \n",
       " 23394  [-0.026658786460757256, -0.056081559509038925,...  90.537048  \n",
       " \n",
       " [1500 rows x 10 columns],\n",
       " [DataIssue(title='Metric: 4.72 | Cause: notes', rows=[16, 1179], severity='medium', columns=['notes', 'variety', 'region'], description='Feature Importances: notes, (1.00); variety, (0.00); region, (0.00)\\n\\nFeature Ranges: variety (mode) = Red Wine; region (mode) = Pomerol, Bordeaux, France'),\n",
       "  DataIssue(title='Metric: 3.57 | Cause: region', rows=[156, 926], severity='medium', columns=['region', 'name', 'notes'], description='Feature Importances: region, (0.72); name, (0.28); notes, (0.00)\\n\\nFeature Ranges: region (mode) = Margaux, Bordeaux, France'),\n",
       "  DataIssue(title='Metric: 3.46 | Cause: name', rows=[90, 1183], severity='medium', columns=['name', 'region', 'notes'], description='Feature Importances: name, (0.60); region, (0.35); notes, (0.05)\\n\\nFeature Ranges: region (mode) = Central Coast, California'),\n",
       "  DataIssue(title='Metric: 3.41 | Cause: name', rows=[67, 801, 1020, 1213], severity='medium', columns=['name', 'notes', 'variety'], description='Feature Importances: name, (1.00); notes, (0.00); variety, (0.00)\\n\\nFeature Ranges: variety (mode) = Red Wine'),\n",
       "  DataIssue(title='Metric: 3.41 | Cause: name', rows=[1099, 1137, 1301], severity='medium', columns=['name', 'notes', 'variety'], description='Feature Importances: name, (0.59); notes, (0.41); variety, (0.00)\\n\\nFeature Ranges: variety (mode) = Red Wine'),\n",
       "  DataIssue(title='Metric: 3.40 | Cause: name', rows=[138, 529], severity='medium', columns=['name', 'notes', 'variety'], description='Feature Importances: name, (0.94); notes, (0.06); variety, (0.00)\\n\\nFeature Ranges: variety (mode) = Red Wine'),\n",
       "  DataIssue(title='Metric: 3.39 | Cause: name', rows=[438, 594, 1201], severity='medium', columns=['name', 'notes', 'variety'], description='Feature Importances: name, (0.69); notes, (0.31); variety, (0.00)\\n\\nFeature Ranges: variety (mode) = Red Wine'),\n",
       "  DataIssue(title='Metric: 3.17 | Cause: name', rows=[427, 1004], severity='medium', columns=['name', 'notes', 'variety'], description='Feature Importances: name, (1.00); notes, (0.00); variety, (0.00)\\n\\nFeature Ranges: variety (mode) = Red Wine'),\n",
       "  DataIssue(title='Metric: 3.09 | Cause: name', rows=[203, 433, 1374], severity='medium', columns=['name', 'notes', 'variety'], description='Feature Importances: name, (1.00); notes, (0.00); variety, (0.00)\\n\\nFeature Ranges: variety (mode) = Red Wine'),\n",
       "  DataIssue(title='Metric: 3.06 | Cause: name', rows=[284, 1013], severity='medium', columns=['name', 'notes', 'variety'], description='Feature Importances: name, (1.00); notes, (0.00); variety, (0.00)\\n\\nFeature Ranges: variety (mode) = Red Wine'),\n",
       "  DataIssue(title='Metric: 3.00 | Cause: name', rows=[815, 882], severity='medium', columns=['name', 'region', 'notes'], description='Feature Importances: name, (0.98); region, (0.02); notes, (0.00)\\n\\nFeature Ranges: region (mode) = St. Estephe, Bordeaux, France'),\n",
       "  DataIssue(title='Metric: 3.00 | Cause: name', rows=[768, 916], severity='medium', columns=['name', 'notes', 'variety'], description='Feature Importances: name, (0.51); notes, (0.49); variety, (0.00)\\n\\nFeature Ranges: variety (mode) = Red Wine'),\n",
       "  DataIssue(title='Metric: 2.97 | Cause: notes', rows=[632, 898], severity='medium', columns=['notes', 'name', 'variety'], description='Feature Importances: notes, (0.77); name, (0.23); variety, (0.00)\\n\\nFeature Ranges: variety (mode) = Red Wine'),\n",
       "  DataIssue(title='Metric: 2.91 | Cause: name', rows=[518, 579, 616], severity='medium', columns=['name', 'notes', 'variety'], description='Feature Importances: name, (0.95); notes, (0.05); variety, (0.00)\\n\\nFeature Ranges: variety (mode) = Red Wine'),\n",
       "  DataIssue(title='Metric: 2.82 | Cause: notes', rows=[187, 245, 268, 453, 999], severity='medium', columns=['notes', 'variety', 'region'], description='Feature Importances: notes, (1.00); variety, (0.00); region, (0.00)\\n\\nFeature Ranges: variety (mode) = Red Wine; region (mode) = Santa Ynez Valley, Santa Barbara, Central Coast, California'),\n",
       "  DataIssue(title='Metric: 2.82 | Cause: notes', rows=[262, 412, 586], severity='medium', columns=['notes', 'region', 'variety'], description='Feature Importances: notes, (0.50); region, (0.50); variety, (0.00)\\n\\nFeature Ranges: region (mode) = Abruzzo, Italy; variety (mode) = Red Wine'),\n",
       "  DataIssue(title='Metric: 2.80 | Cause: notes', rows=[76, 1058, 1085], severity='medium', columns=['notes', 'name', 'variety'], description='Feature Importances: notes, (0.99); name, (0.01); variety, (0.00)\\n\\nFeature Ranges: variety (mode) = Red Wine'),\n",
       "  DataIssue(title='Metric: 2.80 | Cause: notes', rows=[517, 538, 1385, 1484], severity='medium', columns=['notes', 'name', 'region'], description='Feature Importances: notes, (0.55); name, (0.39); region, (0.06)\\n\\nFeature Ranges: region (mode) = Chateauneuf-du-Pape, Rhone, France'),\n",
       "  DataIssue(title='Metric: 2.78 | Cause: notes', rows=[176, 282, 379, 414, 1008, 1302], severity='medium', columns=['notes', 'name', 'variety'], description='Feature Importances: notes, (0.89); name, (0.11); variety, (0.00)\\n\\nFeature Ranges: variety (mode) = White Wine'),\n",
       "  DataIssue(title='Metric: 2.78 | Cause: notes', rows=[213, 775], severity='medium', columns=['notes', 'name', 'variety'], description='Feature Importances: notes, (0.86); name, (0.14); variety, (0.00)\\n\\nFeature Ranges: variety (mode) = Red Wine')],\n",
       " {'sg_projection': renumics.spotlight.media.embedding.Embedding,\n",
       "  'sg_emb_name': renumics.spotlight.media.embedding.Embedding,\n",
       "  'sg_emb_notes': renumics.spotlight.media.embedding.Embedding},\n",
       " Layout(children=[Split(children=[Tab(children=[Table(type='table', name=None, config=TableConfig(active_view='full', visible_columns=None, sort_by_columns=None, order_by_relevance=False), kind='widget')], weight=1.0, kind='tab'), Tab(children=[Similaritymap(type='similaritymap', name=None, config=SimilaritymapConfig(columns=['sg_projection'], reduction_method=None, color_by_column=None, size_by_column=None, filter=False, umap_nn=None, umap_metric=None, umap_min_dist=None, pca_normalization=None, umap_balance=None, umap_is_advanced=False), kind='widget')], weight=1.0, kind='tab'), Tab(children=[Histogram(type='histogram', name='Histogram', config=HistogramConfig(column='region', stack_by_column=None, filter=False), kind='widget')], weight=1.0, kind='tab')], orientation=None, weight=1.0, kind='split'), Split(children=[Tab(children=[Inspector(type='inspector', name='Inspector', config=InspectorConfig(lenses=[Lens(type='TextLens', columns=['name'], name=None, id='5e306ff6-8aa8-4f1b-9853-c03682d2feae'), Lens(type='ScalarView', columns=['region'], name=None, id='fc25a965-b822-47be-9bb5-e35b659e27e0'), Lens(type='ScalarView', columns=['variety'], name=None, id='c1204fca-7f8e-437e-9df4-ce3b0b57d5cf'), Lens(type='TextLens', columns=['notes'], name=None, id='398ddeb8-bd1f-4980-babe-6256481cc358'), Lens(type='ScalarView', columns=['rating'], name=None, id='ed420fa1-b4b2-4a05-9cdf-947147213bfb'), Lens(type='ScalarView', columns=['sg_y_pred'], name=None, id='f0b0c578-296e-4b90-a0d2-d036f887be8b')], num_columns=4), kind='widget')], weight=1.0, kind='tab'), Tab(children=[Issues(type='IssuesWidget', name=None, config=None, kind='widget')], weight=1.0, kind='tab')], orientation=None, weight=1.0, kind='split')], orientation=None))"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get an interactive report on especially bad performing clusters\n",
    "sg.report()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6875806-3f2f-487c-b5fd-8d72683b20bd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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